Exploring the Key Features of Repeating Fast Radio Bursts with Machine Learning

TL;DR

This study uses machine learning, specifically t-SNE, to identify repeating fast radio bursts (FRBs) from the CHIME catalog, revealing key spectral features that distinguish repeaters from non-repeaters and providing a list of candidate repeaters.

Contribution

The paper introduces a novel machine learning approach using t-SNE for classifying FRB repeaters, highlighting spectral morphology as a key feature and expanding the catalog of potential repeaters.

Findings

Spectral running ($r$) is a key feature for identifying repeaters.

Repeaters tend to have narrowband emission, non-repeaters broadband.

Identified 163 repeater candidates, with 5 confirmed.

Abstract

Fast radio bursts (FRBs) are enigmatic high-energy events with unknown origins, which are observationally divided into two categories, i.e., repeaters and non-repeaters. However, there are potentially a number of non-repeaters that may be misclassified, as repeating bursts are missed due to the limited sensitivity and observation periods, thus misleading the investigation of their physical properties. In this work, we propose a repeater identification method based on the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm and apply the classification to the first Canadian Hydrogen Intensity Mapping Experiment Fast Radio Burst (CHIME/FRB) catalog. We find that the spectral morphology parameters, specifically spectral running ($r$), represent the key features for identifying repeaters from the non-repeaters. Also, the results suggest that repeaters are more biased towards narrowband emission, whereas non-repeaters are inclined toward broadband emission. We provide a list of 163 repeater candidates, 5 of which are confirmed with an updated repeater catalog from CHIME/FRB. Our findings improve our understanding of the various properties underlying repeaters and non-repeaters, as well as guidelines for future FRB detection and categorization.